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    "info": {
        "author": "Marcin Pertek",
        "author_email": "kat.zygfryd@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Topic :: Database :: Database Engines/Servers",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: Indexing"
        ],
        "description": "=====\nsetix\n=====\n\nAt its core setix provides a \"set intersection index\", an inverted index data structure designed for storing sets\nof symbols and fast querying of sets intersecting the given set, with sorting based on the number of intersections\nor a similarity measure.\n\nAdditionally, a wrapper for indexing strings is provided in setix.trgm, which implements a trigram index compatible\nwith the PostgreSQL extension pg_trgm.\n\nExamples\n========\n\nUsing a set index:\n\n..  code-block:: python\n    \n    import setix\n    \n    ix = setix.SetIntersectionIndex ()\n    ix.add ((1, 2, 3))\n    ix.add ((1, 2, 4))\n    ix.add ((2, 3, 4))\n    \n    ix.find ((1, 2), 1).get_list()\n    # returns [(2, [(1, 2, 3)]),\n    #          (2, [(1, 2, 4)]),\n    #          (1, [(2, 3, 4)])]\n    # (the order of the first two results can change as they have equal scores)\n\nUsing a trigram index:\n\n..  code-block:: python\n\n    import setix.trgm\n    \n    ix = setix.trgm.TrigramIndex ()\n    ix.add (\"strength\")\n    ix.add (\"strenght\")\n    ix.add (\"strength and honor\")\n    \n    ix.find (\"stremgth\", threshold=1).get_list()\n    # returns [(6, [\"strength and honor\"])\n    #          (6, [\"strength\"]),\n    #          (4, [\"strenght\"])]\n    \n    ix.find_similar (\"stremgth\", threshold=0.1).get_list()\n    # returns [(0.5,  [\"strength\"]),           # 6 intersections / (9 total + 9 total - 6)\n    #          (0.29, [\"strenght\"]),           # 4 intersections / (9 total + 9 total - 4)\n    #          (0.27, [\"strength and honor\"])] # 6 intersections / (9 total + 19 total - 6)\n\nIn general, to search for phrases containing a misspelt word, a threshold of -3*N can be given where N is the number\nof misspellings.\n\n..  code-block:: python\n\n    ix.find (\"stremgth\", threshold=-3).get_list()\n    # returns [(6, [\"strength and honor\"]),\n    #          (6, [\"strength\"])]\n\nBenchmarks\n==========\n\nA benchmark is included in tests/dvd_db_test.py\n\nResults from an Athlon II running at 2.6GHz:\n\nPython 2.7\n----------------------\n\n..  code-block:: none\n\n    In [1]: import tests.dvd_db_test\n    Loading database...\n    Extracted 240577 titles\n    Memory used by data: 107.8MB\n    Building index...\n    CPU time used: 43.1s\n    Unique trigrams indexed: 11352\n    Unique phrases indexed: 228620\n    Memory used by index: 80.9MB\n    \n    In [2]: %timeit list (tests.dvd_db_test.titles.find(\"daft punk\", 8))\n    10 loops, best of 3: 27.8 ms per loop\n    \n    In [3]: %timeit list (tests.dvd_db_test.titles.find(\"daft punk\", 1))\n    10 loops, best of 3: 86.4 ms per loop\n\nPython 3.2\n----------------------\n\n..  code-block:: none\n\n    In [1]: import tests.dvd_db_test\n    Loading database...\n    Extracted 240577 titles\n    Memory used by data: 108.8MB\n    Building index...\n    CPU time used: 45.8s\n    Unique trigrams indexed: 11352\n    Unique phrases indexed: 228620\n    Memory used by index: 86.2MB\n    \n    In [2]: %timeit list (tests.dvd_db_test.titles.find(\"daft punk\", 8))\n    10 loops, best of 3: 27.9 ms per loop\n   \n    In [3]: %timeit list (tests.dvd_db_test.titles.find(\"daft punk\", 1))\n    10 loops, best of 3: 86.3 ms per loop\n\nDVD title list used in the benchmark was obtained from http://www.hometheaterinfo.com/dvdlist.htm\nThanks for making it available.",
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        "keywords": "set,intersection,index,trgm,fuzzy",
        "license": "MIT",
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        "name": "setix",
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        "summary": "Fast data structures for finding intersecting sets and similar strings",
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